RtreemixModel-class {Rtreemix} | R Documentation

## Class "RtreemixModel"

### Description

This class contains all the data needed for characterizing the mutagenetic trees mixture model (mixture parameters, mixture components, ...). The tree components of the model are given as a list of directed `graphNEL` objects.

### Objects from the Class

Objects can be created by calls of the form `new("RtreemixModel", ParentData, Weights, WeightsCI, Resp, CompleteMat, Star, Trees)`. The `RtreemixModel` class extends the `RtreemixData` class and specifies the mutagenetic trees mixture model. If the model is not randomly generated the parent class gives the `RtreemixData` used for learning the mixture model. The directed trees that build up the model are represented as a list of directed `graphNEL` objects, and their weights (the mixture parameters) are given as a numeric vector. This class can also contain other useful information connected with the mixture model like confidence intervals for the mixture parameters and the edge weights (resulting from a bootstrap analysis), an indicator for the presence of the star component, etc. They are all listed in the text below with brief descriptions.

The `ParentData` is an `RtreemixData` object that specifies the data used for estimating the mutagenetic trees mixture model. It is not specified for random mixture models, since they are not estimated from a given dataset but generated randomly.

The `Weights` is a numeric `vector` that contains the mixture parameters of the model. Its length equals the length of the `list` of tree components `Trees`.

The `WeightsCI` is a named `list` with length equal to the length of the `Weights`. Each list element is a numeric `vector` of length two specifying the lower and upper bound of the confidence interval for the corresponding mixture parametar. The confidence intervals are derived using the bootstrap method.

The `Resp` is a numeric `matrix` that specifies the responsibility of each tree component to generate each of the patterns in the `ParentData`. The number of rows in `Resp` is equal to the number of trees in the mixture (the length of the list `Trees`) and the number of columns equals the number of patients in `ParentData`. For random mixture models it is an empty matrix, since they are not estimated from a given dataset.

The `CompleteMat` is a binary `matrix` that specifies the complete data in case some measurements for some patients are missing in the data used for learning the model (the `ParentData`). It has the same size as the matrix specifying the data in `ParentData`. The missing data are estimated in the EM-algorithm used for fitting the mixture model. When there are no missing data in `ParentData`, or the model is randomly generated the `CompleteMat` is an empty matrix.

The `Star` is an indicator of the presence of a noise (star) component and is mostly relevant for models with a single tree component, since it is assumed that mixture models with at least two components always have the noise as a first component. It is of type `logical`.

The `Trees` is a `list` of directed `graphNEL` objects, each for every tree component in the mixture model. The genetic events are represented as nodes in the graphs. The `edgeData` of each tree can have two attributes: `"weight"` and `"ci"`. Please see the help page on `graph-class` and `graphNEL-class` in the package `graph`. The `"weight"` attribute is for edge weight, i.e. the conditional probability that the child event of the edge occured given that the parent event already occured. The `"ci"` attribute is for the bootstrap confidence intervals for the edge weight (a numeric vector with length two).

### Slots

`Weights`:
  Object of class `"numeric"`. The length of the `Weights` must be equal to the length of `Trees`.
`WeightsCI`:
  Object of class `"list"`. The length of the `WeightsCI` must be equal to the length of `Weights`.
`Resp`:
  Object of class `"matrix"`. The number of rows of `Resp` must be identical to the length of `Trees`, and its number of columns to the number of patients in the dataset used for estimating the mixture model (`ParentData`).
`CompleteMat`:
  Object of class `"matrix"`. When specified (when there are missing data) the size of the `CompleteMat` must be equal to the size of the matrix used to estimate the model.
`Star`:
  Object of class `"logical"`.
`Trees`:
  Object of class `"list"`. The length of `Trees` equals the length of `Weights`.

### Extends

Class `"RtreemixData"`, directly.

### Methods

CompleteMat
  `signature(object = "RtreemixModel")`: A method used for obtaining the complete dataset, in case there were any missing measurements for some patients in the dataset used to learn the mixture model.
Resp
  `signature(object = "RtreemixModel")`: A method for obtaining the matrix of responsibilities for the trees to generate each of the samples in the dataset used for learning the model (`ParentData`).
Star
  `signature(object = "RtreemixModel")`: A method for checking the presence of a noise component in the mixture model (informative only for models with one tree component).
Trees
  `signature(object = "RtreemixModel")`: A method for obtaining the tree components of the mixture model as a list of directed `graphNEL` objects.
Weights
  `signature(object = "RtreemixModel")`: A method for obtaining the mixture parameters (the weights of the trees in the model).
Weights<-
  `signature(object = "RtreemixModel")`: A method for replacing the value of the slot `Weights` with a specified `numeric` vector. The components of this vector have to sum up to one.
WeightsCI
  `signature(object = "RtreemixModel")`: A method for obtaining the weights of the mixture parameters.
getData
  `signature(object = "RtreemixModel")`: A method for obtaining the `ParentData` of the mixture model, i.e. the data used for learning the model.
getTree
  `signature(object = "RtreemixModel", k = "numeric")`: A method for obtaining the k-th tree component of the mixture model as a directed `graphNEL` object.
numTrees
  `signature(object = "RtreemixModel")`: A method for obtaining the number of tree components building up the mixture model.

### Author(s)

Jasmina Bogojeska

### References

Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.

### See Also

`RtreemixGPS-class`, `RtreemixStats-class`, `RtreemixData-class`, `RtreemixSim-class`, `fit-methods`, `bootstrap-methods`, `generate-methods`, `comp.models`, `comp.trees`

### Examples

    ## Generate a random RtreemixModel object with 2 components.
    rand.mod <- generate(K = 2, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8))
    show(rand.mod)
    plot(rand.mod) ## plot the tree components of the model
    plot(rand.mod, k = 2) ## plot the second component of the model

    ## Draw data from a specified mixture model.
    draws <- sim(model = rand.mod, no.draws = 200)
    show(draws)

    ## Create an RtreemixModel object by fitting model to the drawn data.
    mod <- fit(data = draws, K = 2, equal.edgeweights = TRUE, noise = TRUE)
    show(mod)

    ## See the values of the slots of the RtreemixModel object.
    Weights(mod)
    Resp(mod)
    CompleteMat(mod)
    Star(mod)
    Trees(mod)
    ## See data used for learning the model.
    getData(mod)
    ## See the number of tree components in the mixture model.
    numTrees(mod)
    ## See a specific tree component k.
    getTree(object = mod, k = 2)
    ## See the conditional probabilities assigned to edges of the second tree component.
    edgeData(getTree(object = mod, k = 2), attr = "weight")
    ## See the probability distribution encoded by the model on the set of all possible patterns.
    distr <- distribution(model = mod)
    distr
    ## Get the probabilities.
    distr$probability
    ## See the probability distribution encoded by the model on the set of all possible patterns
    ## calculated for given sampling mode, and input and output parameters.
    distr1 <- distribution(model = mod, sampling.mode = "exponential", sampling.param = 1, output.param = 1)
    distr1

    ## Create a RtreemixModel and analyze its variance with the bootstrap method.
    mod.boot <- bootstrap(data = draws, K = 2, equal.edgeweights = TRUE, B = 100)

    ## See the confidence intervals for the mixture parameters (the weights).
    WeightsCI(mod.boot)
    ## See the confidence intervals of the conditional probabilities assigned to the edges.
    edgeData(getTree(mod.boot, 2), attr = "ci")

[Package Rtreemix version 1.6.0 Index]